A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh. [email protected].
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
- School of IT, Deakin University, Melbourne Burwood Campus, Melbourne, Australia.
- School of IT, Washington University of Science and Technology, Alexandria, VA, USA.
- School of IT, Washington University of Science and Technology, Alexandria, VA, USA. [email protected].
- AI and Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, 2795, Australia. [email protected].
- AI and Digital Health Technology, Rural Health Research Institute, Charles Sturt University, Orange, 2800, Australia. [email protected].
Abstract
Brain tumors are a critical medical challenge, requiring accurate and timely diagnosis to improve patient outcomes. Misclassification can significantly reduce life expectancy, emphasizing the need for precise diagnostic methods. Manual analysis of extensive magnetic resonance imaging (MRI) datasets is both labor-intensive and time-consuming, underscoring the importance of an efficient deep learning (DL) model to enhance diagnostic accuracy. This study presents an innovative deep ensemble approach based on transfer learning (TL) for effective brain tumor classification. The proposed methodology incorporates comprehensive preprocessing, data balancing through synthetic data generation (SDG), reconstruction and fine-tuning of TL architectures, and ensemble modeling using Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO) used to optimize model weights for enhanced performance. Experiments were performed on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, consisting of 3064 images. The proposed approach demonstrated exceptional performance, achieving classification accuracies of 99.57% with Xception, 99.48% with ResNet50V2, 99.33% with ResNet152V2, 99.39% with InceptionResNetV2, 99.78% with GAWO, and 99.84% with GSWO. The GSWO achieved the highest average accuracy of 99.84% across five-fold cross-validation among other DL models. The comparative analysis highlights the superiority of the proposed model over State of Arts (SOA) works, showcasing its potential to assist neurologists and clinicians in making precise and timely diagnostic decisions. The study concludes that the optimized deep ensemble model is a robust and reliable tool for brain tumor classification.